zhu, et al. proteomics in pathology: pt-swath analysis of ... · # correspondence should be...

Zhu, et al. Proteomics in Pathology: PCT-SWATH analysis of Hepatocellular carcinoma 1 / 26

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Identification of protein abundance changes in biopsy-level hepatocellular carcinoma

tissues using PCT-SWATH

Yi Zhu1,2*, Jiang Zhu3*, Cong Lu3, Ping Sun4, Wei Xie3, Qiushi Zhang1, Liang Yue1, Tiansheng

Zhu1, Guan Ruan1, Ruedi Aebersold2,5#, Shi’ang Huang3#, Tiannan Guo1,2#

1 Westlake Institute for Advanced Study, Hangzhou, Zhejiang, P. R. China

2 Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische

Hochschule (ETH) Zurich, Zurich, Switzerland

3 Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College,

Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China

4 Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong

University of Science and Technology, Wuhan, Hubei, P. R. China

5 Faculty of Science, University of Zürich, Zürich, Switzerland

*, co-first authors

# Correspondence should be addressed to Ruedi Aebersold ([email protected]),

Shi’ang Huang ([email protected]), and Tiannan Guo ([email protected])

peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not. http://dx.doi.org/10.1101/300673doi: bioRxiv preprint first posted online Apr. 13, 2018;

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Abstract

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass

spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19

hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library,

we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue

samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of

variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was

0.9729. We identified protein changes in malignant tissues compared to matched control samples

in HCC patients, and further stratified patient samples into groups with high α-fetoprotein (AFP)

expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13

ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism

in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our

data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and

ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of

performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology

with moderate effort and within a relatively short timeframe.


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Introduction

Hepatocellular carcinoma (HCC) is the fifth frequent malignancy worldwide and ranks as

the third leading cause of cancer-related mortality1. In China, HCC accounts for more than

300,000 deaths every year 2. In early-stage disease, HCC patients’ survival can be significantly

improved by treatment including surgical resection and liver transplant. However, HCC mostly

exhibits no symptom at early stages. Consequently, less than one third of newly diagnosed HCC

patients are eligible for potential curative therapies. Therefore, early diagnosis of HCC is vital

for patient survival, and the identification of biomarkers for early HCC detection is a crucial

clinical need3.

Currently three FDA approved serum biomarkers are recommended to indicate the risk of

liver cancer in high risk populations: α-fetoprotein (AFP), AFP-L3, which is a fucosylated

isoform of AFP, and des-gamma-carboxy-prothrombin (DCP) 4. However, these markers are not

included in the surveillance guidelines published by the American Association for the Study of

Liver Diseases 5 and the European Association for the Study of the Liver 6 because of low

sensitivity and specificity. In China, AFP is used jointly with ultrasonography, computed

tomography (CT) and pathology examination for early screening of HCC patients, as

recommended by the Asian Pacific Association for the Study of the Liver 7. This is based on the

knowledge that the positive rate of AFP in HCC is about 60-80% 8.

Better biomarkers are needed for HCC. The search for HCC biomarkers from clinical

specimens using proteomics approaches has been focused mainly on blood samples 9,10. Besides,

some studies also investigated biological fluids including urine 11 and tissue interstitial fluid 12.

Nevertheless, HCC tissue samples have not been extensively studied by advanced proteomics

methods, even though dysregulated tissue proteins are presumably the main source of potential

blood and biofluidic biomarkers. From the literature, we found that Li, et al studied malignant

and matched non-tumorous tissues (about 100 mg tissue per sample) from eleven HCC patients

using two dimensional LC coupled with shotgun proteomics with a LC gradient of 165 min and

observed upregulation of SET Complex proteins 13. Megger, et al nominated 51 differentially

expressed proteins from about 573 proteins identified by 2D gel electrophoresis and label-free

shotgun proteomic analysis of 7 pairs of HCC tissue samples, 14. In a more recent study,

Naboulsi W. et al. identified and quantified 2,736 proteins from 19 pairs of HCC tumorous and


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adjacent non-tumorous tissue using the label-free shotgun proteomics over a 98-min LC gradient

15. Gao et al 16 applied SWATH-MS to study 14 pairs of HCC and non-HCC tissues and

quantified 4,216 proteins in at least one biological replicates and 1,903 proteins in at least three

out of five biological replicates using a 120-min LC gradient. For each sample, about 200 mg

tissue was used. These studies are generally of relatively low throughput and consumes relatively

large amount of tissue samples.

We have recently developed a methodology integrating pressure cycling technology

(PCT) and SWATH mass spectrometry (MS) for the rapid acquisition of proteotypes, defined as

the acute quantitative state of a proteome, from biopsy-level tissue samples (about 2 mg wet

weight) 17. PCT technology produces high pressure (up to 45,000 p.s.i.) and lyses tissue samples

by oscillating the pressure between low and high values, thereby allowing tissue lysis and protein

digestion to occur in the same PCT-MicroTube. The method can be applied in a semi-automated

and standardized manner and achieves relatively high sample throughput 17-19. SWATH-MS is an

emerging data-independent acquisition (DIA) mass spectrometric method that offers a high

degree of quantitative accuracy, proteomic coverage, reproducibility of proteome coverage and

sample-throughput 20. The PCT-SWATH methodology was further demonstrated to be

applicable to rapid proteotype acquisition from sub milligrams of tissue samples 19. In our first

study that demonstrated the technology, the PCT-SWATH method was applied to process and

convert 18 biopsy samples from nine patients with renal cell carcinoma into SWATH-MS

fragment ion maps 17. With a two-hour LC gradient, a 32 fixed SWATH windows setting, and

the OpenSWATH data analysis tool 21, about 2,000 SwissProt proteins were identified with a

high degree of reproducibility across all samples 17. More recently, a more advanced and

miniaturized device, i.e. PCT-MicroPestle 18 was introduced providing for higher efficiency of

protein extraction and peptide generation from biopsy-level tissues.

In this study, we firstly improved the PCT-SWATH methodology by adopting a 45-min

LC gradient and an acquisition method using 67 variable SWATH windows to increase the

sample throughput without compromising the proteomic depth. Then we applied the improved

workflow to analyze peptide samples from a cohort of HCC patients in technical duplicate with

unprecedented reproducibility and throughput.


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Materials and Methods

Patients and tissue samples

38 fresh frozen tissues from 19 HCC patients were collected from Union Hospital, Tongji

Medical College, Huazhong University of Science and Technology, Wuhan, China. The clinical

and pathological data for these patients are provided in Table 1. For each patient, two tissue

biopsy punches (with dimensions of 5*5*5 mm3) including a tumorous tissue and a non-

tumorous tissue from an adjacent region as determined by histomorphological examination were

collected. All 38 tissue samples were collected from hepatectomy specimens within 1 hour after

surgical removal, snap frozen and stored at -80℃ until proteomic analysis. The sample collection

was approved by institutional review board of the Union Hospital in Wuhan, China. This study

was approved by Ethics Committee of Tongji Medical College, Huazhong University of Science

and Technology.

PCT-assisted sample preparation

For each sample, about 1.6 ~ 2.5 mg (wet weight) of frozen tissue was processed based

on the protocol described previously 17,18. Briefly, tissues were lysed with the PCT-MicroPestle

device (Pressure BioSciences Inc, South Easton, MA) in 30 µL of lysis buffer composed of 8M

urea, 0.1 M ammonium bicarbonate, and protease inhibitor (Roche) in a barocycler HUB440

(Pressure BioSciences Inc). The tissue lysis was performed under a program consisting of 60

oscillating cycles, with each cycle consisting 50 s of high pressure at 45,000psi and 10 s of

ambient pressure at 33℃. Then the extracted proteins were reduced and alkylated by incubation

with 10mM Tris (2-carboxyethyl) phosphine (TCEP) and 20 mM Iodoacetamide (IAA) at 25°C.

This was followed by vortexing at 600 rpm for 30 min in the dark, at ambient pressure.

Afterwards, digestion was first performed with Lys-C (Wako; enzyme-to-substrate ratio, 1:40) in

the barocycler (20,000 psi, 50 s high pressure and 10 s ambient pressure, 45 cycles), followed by

trypsin (Promega, enzyme-to-substrate ratio, 1:50) digestion in the barocycler (20,000 psi, 50 s

high pressure and 10 s ambient pressure, 90 cycles). After digestion, the peptides were acidified

with trifluoroacetic acid (TFA) to pH 2-3 and cleaned with SEP-PAK C18 (1cc 50mg) cartridges

(Waters).

SWATH-MS


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The sample samples were spiked with iRT peptides (Biognosis, Zurich, Switzerland) at a

1:10 (vol/vol) ratio. One microgram of peptide sample was injected to an Eksigent 1D+ Nano LC

systems (Eksigent, Dublin, CA) and analyzed in a 5600 TripleTOF mass spectrometer (SCIEX)

in SWATH mode as described previously 17. We optimized the LC gradient to 45 min

(Supplementary Fig. 1), and the SWATH acquisition scheme to 67 variable windows

(Supplementary Table 1). The other SWATH parameters were set exactly as in our previous

study 17, except that the ion accumulation time for each SWATH window was 40 ms. Ion

accumulation time for peptide precursors was set at 50ms. The 38 samples were injected into the

mass spec in randomized sequence once and then the same sequence was injected again to obtain

a duplicate analysis. After each gradient, the column was washed twice using ramping gradient

to minimize carry-over (Supplementary Fig. 1). Mass calibration using beta-gal was performed

every fourth injection.

OpenSWATH analysis

OpenSWATH (version 2014-12-01-154112) 22 was performed as described previously 17

except that a pan-human library (32 windows version) 21 was used to search the SWATH data in

the iPortal platform 23. The details of the pan-human library, containing 139,449 peptides from

10,316 proteins, have been described previously21. The default parameters as used previously 17

are provided in Supplementary Table 2.

Pathway and process enrichment analysis

Pathway and process enrichment analysis of the differentially abundant proteins were

performed using Metascape (http://metascape.org). Terms with p-value < 0.01, minimum count

3, and enrichment factor > 1.5 (enrichment factor is the ratio between observed count and the

count expected by chance) are collected and grouped into clusters based on their membership

similarities. To further capture the relationship among terms, a subset of enriched terms was

selected and rendered as a network plot, where terms with similarity > 0.3 are connected by

edges.

Protein-protein interaction enrichment analysis was carried out for each given gene list

(http://metascape.org). The resultant network contains the subset of proteins that form physical

interactions with at least another list member. Molecular Complex Detection (MCODE)

algorithm 24 was applied to identify densely connected network components. Pathway and


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biological process enrichment analysis was applied to each MCODE components independently

and the three best-scoring (by p-value) terms were retained as the functional description of the

corresponding components.

Statistics

In volcano plots, paired Student t test was used to compute p values followed by Benjamini &

Hochberg correction in R.

Data deposition

All the SWATH Data files, library and original OpenSWATH results are deposited in

PRIDE project 25: PXD004873. Reviewer account details: Username:

[email protected]; Password: WTpBL22V

(Note for reviewers: Prior to publication, this data set only be found in PRIDE after the

reviewer logs into PRIDE using the username and password provided above. This data set

cannot be found without logging in.)


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Results and Discussion

Identification of peptides and proteins by PCT-SWATH

To start we extracted proteins from 38 biopsy-level liver tissue samples and produced

tryptic peptides using the PCT method 17,26. We measured the wet weight of each tissue sample

and the total amount of peptide mass generated from the tissue and computed the peptide yield in

weight percent per milligram of tissue. The results shown in Fig. 1A indicate that the peptide

yield for the 38 samples varied from 1.0% to 9.5%, with an average value of 6.0%. 59.6 µg of

total peptide mass per milligram wet liver tissue were obtained on average. The peptide yield in

tumor tissue (4.9% on average) was lower than that of normal tissue (8.1% on average, p value =

0.00037) (Fig. 1A). The results further showed that tumorous samples exhibited more variability

in terms of peptide yield compared to the normal tissue samples. The coefficient of variation

(CV) of peptide yield was 15.8% for normal tissue samples (n = 19), 40.7% for tumorous

samples (n = 19), and 32.9% overall (n = 38) (Fig. 1B), demonstrating the heterogeneity of liver

tumors.

Each peptide sample was then analyzed using SWATH-MS in technical duplicate, using

a 45-min LC gradient and a 67 variable SWATH window acquisition scheme (Supplementary

Table 1) which was optimized based on the ion density map of human proteins extracted from

HCC tissues. With this SWATH window setting, up to 20 samples were analyzed per MS

instrument per 24 hours. Mass calibration using beta-gal was performed every fourth injection.

The MS analysis of the 76 SWATH runs were completed in about 4 working days.

After searching the resulting raw data files against the pan-human SWATH assay library

21, 2,579 unique proteotypic SwissProt proteins with 11,787 unique peptides were identified at a

false discovery rate (FDR) below 1% using OpenSWATH 21 (Supplementary Table 3).

Comparable numbers of proteins and peptides were quantified from both non-tumorous and

tumorous type of tissue samples (Fig. 2A and 2B). In this study, the PCT-SWATH throughput

was further increased compared to the previous work 17. The protein numbers identified per

sample was slightly higher than those obtained from human kidney tissues 17.

Variability of protein expression between tumor and non-tumorous tissues


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To evaluate the quantitative accuracy of the method, we computed the global Pearson

correlation of the two technical replicates at the peptide level for all samples after removing

missing values. The R2 was 0.9729 (Fig. 2C), indicating that our label-free method could

quantify peptides with high degree of reproducibility. The global view of protein quantification

is shown in Fig. 2D. It shows that all the technical replicates were clustered together

respectively.

We then analyzed the overall variation of protein abundance for all 2,579 quantified

proteins from tumor tissues, adjacent non-tumorous (normal) tissues and all samples,

respectively (Fig. 3A). The data showed that the CV of tumorous tissue samples was slightly

greater than that of adjacent non-tumorous tissues, indicating more overall variability of protein

expression in tumors. We next checked the relationship between protein abundance and the

overall variation among these samples and observed that low abundance proteins exhibited a

higher degree of overall variability (Fig. 3B, 3C, 3D). In Fig. 2C, proteins of low abundance

demonstrated comparable reproducibility with high abundance proteins, therefore we mainly

attributed the higher variability in tumor tissue to biological causes.

Differentially regulated proteins characterized in the HCC cohort

We further explored differentially expressed proteins between tumorous and non-

tumorous samples. A total of 541 proteins showed significant differential abundance (Fig. 4A).

Of these, 381 proteins showed increased abundance and 160 proteins showed decreased

abundance in tumorous tissues compared to non-tumorous tissues. Pathway enrichment analysis

of the regulated proteins in HCC tumorous tissues was performed by Metascape. We found that

proteins with increased abundance were highly enriched in mRNA related processes including

mRNA metabolic process, peptide metabolic process, ribonucleoprotein complex biogenesis, and

nuclear transport. The proteins with decreased abundance were found to be mostly involved in

small molecule metabolic processes such as small molecule catabolic process, monocarboxylic

acid metabolic process, and carbon metabolism.

Next, we carried out protein-protein interaction network analysis using Metascape. The

resultant network contains the subset of proteins that form physical interactions with at least

another protein showing altered abundance. Heat shock proteins (HSP) were found to be

increased (Fig. 4B). HSP proteins constitute a family of highly conserved stress response


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proteins that can protect cells and induce them to repair damage caused by a variety of stress 27.

Several members of the HSP family have been reported to be overexpressed in a wide range of

human cancers 28. The HSP70 family contains at least eight homologous chaperone proteins that

differ from each other by amino acid sequence, expression level and sub-cellular localization 29.

Hsp70-1 is an essential molecular chaperon upregulated in response to heat, stress, or cell

survival protection and its function is tightly controlled by heat shock factor-1 (HSF-1). It has

been demonstrated that 14-3-3σ protein induces HSP70 via a β-catenin/HSF-1-dependent

pathway, which consequentially modulates HCC. The HSF-1/Hsp70-1 protein complex is

therefore considered to be targeted for developing a prognostic tool for HCC 30.

We also observed that proteins related to mitochondrial translation, specifically the

mitochondrial ribosomal proteins (MRPs), were among the most strongly upregulated protein

clusters (Fig. 4C). Tumor initiation and progression in cancer cells involve the development of

mechanisms to inhibit apoptosis at multiple stages, whereas mitochondria play crucial roles in

the induction of apoptosis and programmed cell death. This paradigm is central to malignant

cellular transformation because altered mitochondrial function and defective apoptosis are well-

known hallmarks of cancer cells. It has also been reported that the expression of genes encoding

MRPs, mitoribosome assembly factors and mitochondrial translation factors is modulated in

multiple cancers, which is linked to tumorigenesis and metastasis 31. Mini-chromosome

maintenance complex component 7 (MCM7) was also found to be overexpressed (Fig. 4C).

MCM7 plays an essential role in initiating DNA replication. DNA replication is a central process

in cell proliferation, while aberrant DNA replication is a driving force of oncogenesis. It has

been reported that overexpressed MCM7 is associated with a poor prognosis of HCC patients,

and MCM7 promotes HCC cell proliferation via upregulating MAPK-cyclin D1 pathway both in

vitro and in vivo 32. In addition, the hallmark MYC targets proteins, and the cell cycle checkpoint

proteins involved in G2/M transitions, are also among the upregulated protein networks.

Small molecule catabolic processes define the group of proteins that showed the strongest

reduction of abundance in the HCC samples tested (Fig. 4D). Our data showed that proteins

involved in glycogen metabolism pathway were suppressed in HCC samples. These proteins

include the glycogen synthesis enzymes including phosphoglucomutase-1 (PGM1), and UTP-

glucose-1-phosphate uridylyltransferase (UGP2), as well as the glycogen degradation enzyme

liver glycogen phosphorylase (PYGL). They are downregulated in tumorous tissues compared to


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their matched non-tumorous controls, indicating the reprogramming of glycogen metabolism in

HCC which suppresses the turn-over of glycogen. However, our data did not indicate whether

this reprogramming changed the direction of glycogen storage.

Both cystathionine β-synthase (CBS) and cystathionine-γ-lyase (CTH) were found at

decreased abundance in the HCC samples tested (Fig. 4D). CBS and CTH are abundant proteins

in the liver, endogenously produce hydrogen sulfide (H2S), a gasotransmitter modulating

synaptic transmission, vasorelaxation, angiogenesis, inflammation, and cellular bioenergetics.

Regulation of H2S influences lipid metabolism, glucose metabolism, oxidative stress, and

mitochondrial bioenergetics 33. Suppression of CBS transcripts has been reported in a study of

120 HCC patients compared to non-cancerous liver tissue 34. Reduced CBS transcripts

expression is significantly correlated with high tumor stage, high Edmondson grade, and high

AFP level 34. However, neither the CBS nor the CTH protein level has yet been investigated in

HCC tissue. Our data suggests downregulation of H2S production via suppressing CBS and CTH

in HCC tissues.

The ethanol metabolism was also found to be downregulated (Fig. 4D). Aldehyde

dehydrogenase (ALDH) is a gene superfamily that is responsible for the detoxification of

biogenic and xenogenic aldehydes. Both ALDH2 and ALDH1B1 proteins were significantly

downregulated. The ethanol detoxifying pathway in humans occurs mainly in the liver and is

carried out by two enzymatic steps. In the first step ethanol is metabolized quickly by alcohol

dehydrogenase (ADH) to generate acetaldehyde, and the latter is then metabolized by the

mitochondrial ALDH2 to acetate 35. ALDH2 is expressed ubiquitously in all tissues but is most

abundant in the liver and found in high amounts in organs that require high mitochondrial

oxidative phosphorylation. The downregulation of ALDH in tumorous liver tissue will likely

accumulate toxin acetaldehyde and promote tumorigenesis.

In summary, liver is the pivotal organ for molecular synthesis and the metabolic hub of

human beings. Our data show that the molecular synthesis related pathways are enhanced, while

most major metabolic processes were suppressed in the HCC tissues tested, suggesting a

biochemical imbalance in HCC cells.

Proteins with altered abundance in HCC patients with high serum AFP


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Next, we explored the molecular pathogenesis of HCC by associating protein expression

with various clinic-pathological characteristics of HCC. AFP is the most widely used tumor

biomarker currently available for early detection of HCC, and the widely accepted threshold of

serum AFP is 20 ng/mL. It has been reported that at this threshold, serum AFP had a sensitivity

of 41-65% and specificity of 80-94% 36. We grouped the 19 HCC patients in our cohort into two

groups, according to an AFP-cutoff of 20 ng/mL (Table 1), computed the fold change of protein

expression between tumorous and non-tumorous tissues, and checked the protein regulation

pattern of 11 patients with high AFP level (> 20 ng/ml) and six patients with low AFP level (<

20 ng/ml), respectively. Two patients without serum AFP examination value at surgical

operation were excluded from these analyses. Although a considerable proportion of HCC

patients in the cohort do not have elevated serum AFP, and serum AFP can increase in patients

with diseases other than HCC 4, high expression of AFP in serum correlated with high cell

proliferation, high angiogenesis and low apoptosis and was associated with poor prognosis of

HCC patients 37. Indeed, we found in our data set that the low serum AFP level was closely

related to higher survival rate of the patients (5 out of 7 patients have recurrence free survival),

especially when there was no portal vein thrombosis detected by CT at diagnosis, as is shown in

Table 1. On the other hand, in the AFP-high patient group, 5 of 10 patients were dead and 2

patients suffered from tumor recurrence, displaying poorer survival (Table 1). Nevertheless,

statistical analysis was not feasible due to small sample size.

With respect to the AFP-high patient group, 419 proteins showed increased abundance

and 192 proteins showed decreased abundance (P value < 0.05) in tumorous samples compared

non-tumorous samples. Pathway enrichment and network analysis of these differentially

expressed proteins revealed similar results with the above 541 regulated proteins from all 19

HCC patients (Fig. 5A). In the AFP-low subgroup, our data showed no protein significantly

regulated between the tumorous and non-tumorous tissues, probably due to the small sample

size.

In the AFP-high subgroup (Fig. 5B), we identified additional downregulated proteins

including methylene tetrahydrofolate dehydrogenase 1 (MTHFD1), which is an enzyme

identified in the one-carbon (1C) metabolism pathway. Another enzyme in the pathway, i.e.

serine hydroxymethyltransferase (SHMT1), is also downregulated (Fig. 4D). The data suggest

reprogramming of one-carbon metabolism in HCC. This is consistent with the Liverome study


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reporting that downregulation of MTHFD1 is prevalent in proliferative and poorly differentiated

HCCs38 .

We also observed downregulation of a tumor suppressor protein regucalcin (RGN) which

modulates multiple protein kinases and phosphatases in cancers including HCC 39. Transcripts

analysis reports that prolonged survival in HCC patients is associated with increased RGN gene

expression 40. Our data at protein level further consolidate the potential roles of RGN in HCC.

Two proteins involved in lipid metabolism showed decreased expression in the AFP-high

HCC tissues. ACAA2 is a mitochondrial enzyme involved in the fatty acid metabolism pathway.

ANXA6 is a lipid-binding protein highly expressed in the liver, regulating cholesterol

homeostasis and signaling pathways with a role in liver physiology. A recent study found that

ANXA6 was downregulated in HCC tissues compared to non-tumorous tissues in 18 HCC

patients 41. The data indicate dysfunction of lipid metabolism in HCC tissues.

Quantitative proteomics analysis of Hepatitis B virus positive (HBV+) HCC subgroup

Chronic HBV is the most common cause of HCC in China. Approximately 80% of HCC

develops from liver cirrhosis, which predominantly progresses from HBV in China 42. We found

that 106 and 75 proteins showed increased or decreased abundance, respectively, in 11 HCC

patients with HBV infection (Fig. 5C) compared to those without HBV infection. Subsequent

pathway and protein interaction analysis by Metascape revealed a number of interesting protein

clusters (Fig. 5D) that showed a close relationship to liver cirrhosis in patients with chronic HBV

infection. Pyruvate kinase M (PKM) was found to be upregulated in this HBV+ subgroup (Fig.

5D). PKM is a protein kinase that catalyzes the final step in glycolysis by transferring the

phosphate from phosphoenolpyruvate (PEP) to ADP, thereby generating pyruvate and ATP 43.

PKM has two isoforms, PKM1 and PKM2. PKM2 plays an important role in metabolic

alterations related to inflammation and cancer. The ATP generation by PKM2, unlike

mitochondrial respiration, is independent of oxygen and thus allows tumor cells to grow in

hypoxic conditions. Cancer cells are characterized by high glycolytic rates to support energy

regeneration and anabolic metabolism, along with the high expression of PKM2 44. It has been

shown in a recent report that the anti-apoptotic protein poly(ADP-ribose) polymerase (PARP)14

promotes aerobic glycolysis in HCC by maintaining low activity of PKM2, and that the

PARP14-JNK1-PKM2 regulatory axis links apoptosis and metabolism 45. In agreement with our


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finding that HBV+ tumors expressed higher level of PKM, there is another report showing that

high PKM2 expression was more frequently found in cirrhotic liver caused by HBV infection

than in non-cirrhotic liver, and that PKM2 overexpression was associated with poor survival

rates in HCC patients with cirrhotic liver (CL) 46.

Cytidine triphosphate synthase 1 (CTPS1) is another example of a protein that showed

higher expression in the HBV+ HCC subgroup in this study compared to patients without HBV

infection (Fig. 5D). CTPS1 catalyzes the rate-limiting step in the de novo CTP synthetic

pathway, in which a UTP is converted into CTP with the consumption of glutamine and ATP. In

fact, upregulated CTPS1 activity has been observed in multiple human and rodent tumors 47,

promoting tumor transformation and progression 48. Recently, researchers focus on the CTPS

cytoophidium that is a filamentous intracellular macrostructure aggregated by CTPS, and is able

to promote the activity of CTPS 49. A recent study examined the presence of CTPS cytoophidia

in the tumorous and the adjacent non-tumorous tissues of HCC patients, and found that many

cytoophidia were observed in 28% of the HCC tumor samples but not the adjacent hepatocyte

population 50. In addition, they found that the high expression of HSP90 was correlated with the

presence of CTPS cytoophidia, which is consistent to our findings that heat shock proteins

(HSPA1L and HSPA8) and CTPS1 are all upregulated.

Fructose-bisphosphate aldolase B (ALDOB) is an enzyme for glucose and fructose

metabolism. In a cohort of 313 HCC patients, ALDOB was found downregulated in HCC tissue

using tissue microarray and immunohistochemistry (IHC) technologies, and its downregulation

was significantly correlated with HCC progression 51. Furthermore, ALDOB has been found to

be a binding protein of the S region of the HbsAg 52. Our data showed that ALDOB expression

suppressed in HBV+ HCC patients, suggesting a potentially interesting mechanistic linkage

between HCC and HBV infection, which might be utilized as therapeutic target.

Summary of regulated proteins in HCC samples

We summarize all differentially expressed proteins in HCC samples including the AFP-

high group and HBV+ group (Fig. 6A). Interestingly, the proteins with increased expression

concentrated in three types of pathways: a) production of DNA, mRNA and protein; b)

oncogenic signaling pathways and apoptosis; c) immune response. This suggests enhanced

proliferation of tumor cells in the malignant tissues. In contrast, proteins participated in various


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metabolism pathways were down-regulated, indicating the suppression of metabolic functionality

in liver during oncogenesis. Compared to a previous report of HCC tissue proteome using

SWATH-MS 16, our data are acquired in a shorter time frame but identified similar protein

alternation. For instance, we also observed malignant tissue displayed increased abundance of

glucose-6-phophate dehydrogenase (G6PD) and perilipin 2 (PLIN2), as well as downregulation

of formimidoyltransferase cyclodeaminase (FTCD) and phosphoenolpyruvate carboxykinase 2

(PCK2, mitochondrial).

From literature review of all the regulated proteins in Fig. 6A, we identified 16 proteins

of clinical relevance to HCC (Fig. 6B). For instance, the overexpression of PKM2 was more

frequently found in cirrhotic liver resulted from HBV infection, and ALDOB, whose interaction

with HbsAg might be applied as a potential therapeutic target for HBV-related hepatitis or HCC.

We calculated the expression level of these 16 proteins between tumorous and the adjacent non-

tumorous (named as normal) tissue, as was shown in Fig. 6B, the log10 MS intensity, with the p

value for each pair of comparison between normal and tumor. Although this study is not the first

to identify the relevance of these proteins to liver cancer, our study further consolidates their

roles in HCC tissues, and more importantly, we demonstrate that this PCT-SWATH-based high-

throughput methodology allows measurement of clinically relevant proteins from biopsy-level

tissue samples.

Conclusion

In conclusion, here we report an optimized PCT-SWATH workflow enabling analysis of

clinical tissue specimens with unprecedented sample throughput without compromise of

quantitative accuracy and proteomic coverage. The entire process (from receiving 38 tissue

samples to completing the data acquisition of 76 SWATH files) took about 9 working days,

enabling timely measurement in a clinical scenario. Our study reveals a regulated protein

landscape in this HCC cohort. Remarkably, proteins with increased abundance are mainly related

to mass production, oncogenic signaling and immunity, where metabolic proteins show lower

expression. We identified 16 dysregulated proteins of high clinical relevance to HCC, as reported

by the literature and substantiated by our data. The study demonstrates that the PCT-SWATH

methodology has potential to be practically applied in clinical research to generate high quality

proteomic datasets and measure clinically relevant proteins from biopsy-level tissue samples.


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Author contributions

T.G. conceived and coordinated the project. J.Z., C.L., W.X., P.S. performed the PCT-

based sample preparation. J.Z., Y.Z., Q.Z., T.Z., T.G. performed the data analysis. Y.Z., J.Z.,

R.A., S.H., T.G. wrote the manuscript with inputs from all co-authors. R.A., S.H. and T.G.

supervised the project.

Acknowledgements

This study was supported the National Natural Science Foundation, P.R China

(NNSF/81200348/2013), the Swiss National Science Foundation (SNF 166435 MitoModules to

R.A) ERC PROTEOMICS4D (project-no 670821), start-up package from Westlake Institute for

Advanced Study to T.G.. Y. Z. was supported by a fellowship from SignalX (SystemsX RTD

2013/156) and Eu PrECISE (project-no 668858).

Conflict of Interest

R.A. holds shares of Biognosys AG, which operates in the field covered by the article.

The research groups of R.A. and T.G. are supported by AB SCIEX, which provides access to

prototype instrumentation, and Pressure Biosciences, which provides access to advanced sample

preparation instrumentation.


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17

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Table 1. Clinicopathological data of the 19 HCC patients.Pati

ent

ID

Gende

r

Age

(year

)

AFP

(ng/ml)

Albumi

n (g/l)

Heptitis

Virus

Infectio

n

HBV DNA

copy or

HCV RNA

Load

Number

of

Tumors

Tumor

size (the

largest,

cm)

Portal

vein

thrombosi

s (CT)

Microscope

thrombus

(after

surgery)

Metastasis Pathologic type

Surgery time

(MM/DD/YY

)

Prognosis

（1st

time）

Prognosis

(Dec-

2017）

Date of

Death

1 F 50 1728.00 31.7 HCV-LC <1000 2 >5 NA NA Hepatic

membrane Moderately-poorly differentiated hepatocellular carcinoma 17-Sep-2015 Jan-2016

Tumor

recurrence

after

resection

2 M 59 13.20 28.1 HBV-LC <100 3 5.9*5 NA NA incisal edge Moderately-poorly differentiated hepatocellular carcinoma 21-Sep-2015 30-June-

2017

Recurrenc

e free

survival

3 M 53 1.00 32.5 HBV-LC 1.61*10^5 1 10*10 YES YES

Hepatic

membrane

，incisal

edge

Moderately-poorly differentiated hepatocellular carcinoma 21-Sep-2015 NA dead date

unknown

4 M 45 50347.40 36.7 HBV-LC 4.93*10^2 1 8*6 YES YES

Hepatic membrane

，incisal

edge

Poorly differentiated hepatocellular carcinoma 21-Sep-2015 NA dead Jul-16

5 M 67 81.70 33.6 HBV 6.11*10^5 1 5.4*4.2 NA NA lymph node Moderately-poorly differentiated hepatocellular carcinoma 23-Sep-2015 24-Dec-

2015 dead 19-Aug-16

6 M 62 20.90 32 HBV <100 2 16.6*13.

9 YES YES

Hepatic

membrane Moderately-poorly differentiated hepatocellular carcinoma 14-Oct-2015

23-Nov-

2015 dead 22-Aug-17

7 M 47 89.20 36.2 / / 1 8*6 NA NA Hepatic

membrane Moderately-highly differentiated hepatocellular carcinoma 19-Oct-2015 NA

Recurrenc

e free

survival

8 F 48 7185.30 38.1 HBV 1.76*10^5 1 3*3 NA NA Hepatic

membrane Moderately differentiated hepatocellular carcinoma 22-Oct-2015

16-March-

2016

Recurrenc

e free

survival

9 M 69 1.40 32.5 / / 1 3.5*3.3 NA NA NA Moderately differentiated hepatocellular carcinoma 26-Oct-2015 NA

Recurrenc

e free

survival

10 M 80 2.30 30 HBV 2.01*10^5 1 6*4 YES YES incisal edge Moderately-poorly differentiated hepatocellular carcinoma 5-Nov-2015 12-Dec-

2016

Recurrenc

e free

survival

11 M 63 4.70 40.7 / / 1 5 NA NA NA Moderately differentiated hepatocellular carcinoma 5-Nov-2015 16-May-

2017

Recurrenc

e free

survival

12 M 54 38.21 49.1 HBV 10.885*10^

3 1 4 NA NA NA Moderately differentiated hepatocellular carcinoma 11-Nov-2015 NA

Recurrenc

e free

survival

13 M 63 No data No data HCV,

cured / 1 12 YES YES not rule out differentiated hepatocellular carcinoma 9-Nov-2015

15-June-

2017

Tumor

recurrence

after

resection

14 M 67 4018.30 31.3 HBV / 3 6*7 NA NA Hepatic

membrane Mulifocality moderately differentiated hepatocellular carcinoma 16-Nov-2015 NA dead

9-Dec-

2016

15 M 49 74913.40 34.8 / / 2 3*5 YES YES Hepatic

membrane Poorly differentiated hepatocellular carcinoma 23-Nov-2015 NA dead

date

unknown

16 M 53 17305.70 44.9 HBV 5.24*10^4 1 11*11 NA NA NA Highly-moderately differentiated hepatocellular carcinoma 26-Nov-2015 2-Feb-2016

Tumor

recurrence

after

resection

17 M 43 No data No data HBV-LC <100 1 3*3 NA NA NA Moderately differentiated hepatocellular carcinoma 1-Dec-2015 9-Nov-

2016

Recurrenc

e free

survival

18 M 50 >80000 44 / / 1 10 NA YES gallbladder Poorly differentiated hepatocellular carcinoma 2-Dec-2015 4-March-

2016 dead

date

unknown

19 M 57 4.46 43.8 / / 1 3*4 NA NA NA Poorly differentiated hepatocellular carcinoma 2-Dec-2015 NA

Recurrenc

e free

survival


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21

Figure 1

Figure 1. Performance of PCT-assisted peptide preparation. (A) Yield of peptide in two

types of liver tissue samples. Y-axis shows the peptide yield in percentage of peptide (µg) per

milligram (mg) tissue. (B) Coefficient of variation (CV) of peptide yield in two types of tissues

and all samples.


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22

Figure 2

Figure 2. Peptide and protein identification and quantification. (A) Number of peptides

identified in each sample (76 SWATH runs from 38 of samples from 19 pairs of HCC patient

tissue). (B) Number of proteins identified in each sample. (C) Global Pearson correlation of

technical replicates of peptide quantification for samples in all samples. Values are log2

transformed protein intensity. Missing values were excluded from this analysis. (D) Heatmap

overview of protein abundance patterns in 76 samples. Protein intensity values were log10

transformed and subjected to unsupervised-clustering in two axes. (P, patient ID; N, non-

tumorous; T, tumorous; a, the first technical replicate; b, the second technical replicate; for

example, P1Na, the technical replicate run No. 1 of non-tumorous tissue from patient No. 1).


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23

Figure 3

Figure 3. Variability of protein expression in liver tissues. (A) Distribution of protein

abundance variability, quantified as percentage of CV values, in tissue samples. B, C and D

show correlation of CV of protein abundance variability and protein abundance of all samples,

non-tumorous samples and tumorous samples, respectively.


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Figure 4

Figure 4. Significantly regulated proteins in all tumorous tissues versus non-tumorous

tissues. (A) Volcano plot of proteins expressed in tumorous tissue samples and their matched

non-tumorous samples. Each data point represents the ratio of tumorous to adjacent non-

tumorous tissue observed for a protein. The horizontal line at −log10 (p-value) = 1.3 indicates p=

0.05. Data points above the line (p < 0.05) are considered to be significantly regulated. The two

vertical lines at log2 ratio (tumorous to non-tumorous) = ±1 where the fold change is equivalent

to 2 indicate the threshold for up-/downregulation. The upper left quadrant (p < 0.05 and fold

change <0.5) represent the downregulated proteins whereas the upper right quadrant indicates the

upregulated proteins in tumorous tissue. N, non-tumorous; T, tumorous. (B) and (C) enriched

significantly upregulated protein networks. (D) enriched significantly downregulated protein

network.


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Figure 5

Figure 5. Significantly regulated proteins in tumorous tissues versus non-tumorous tissues

grouped by the blood AFP level and HBV infection status. Volcano plots of proteins

expressed in tumorous tissue samples and their matched non-tumorous samples in AFP-high

patient group (A) and HBV infection status (C). (B) and (D) show the enriched significantly

downregulated protein network in the AFP-high patient group and the HBV+ group, respectively.


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Figure 6

Figure 6. Summary of regulated proteins and pathways in HCC tissues compared to

matched control samples. (A) All proteins found regulated in HCC tissues, including AFP-high

group and HBV+ group, were inspected in literature. Regulated pathways and their component

proteins are displayed. (B) 16 selected proteins significantly regulated in HCC tissues compared

to matched non-tumorous samples. For each protein, its expression, after 1og10 transformation,

in non-tumorous (N) and tumorous (T) tissues are shown in boxplots. Student t test was used to

compute the significance between N and T samples.


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